This project entails leveraging SQL Server Management Studio for the analysis of sales data, employing RFM analysis techniques to gain insights into the vehicle sales industry. Subsequently, Tableau is utilized for visualizations to facilitate a comprehensive understanding of the data.
Before commencing the project, I initiated a connection to an existing database and imported the pertinent flat file that serves as the basis for my SQL queries. The flat file encompasses comprehensive vehicle sales data.
Following the successful population of the sales data into the designated database, I am now able to manipulate the dataset to extract meaningful information.
Project Point 1: An initial step involves scrutinizing the data to identify the essential fields for analysis, employing the 'SELECT distinct' query to identify unique values. This process enables a focused extraction of relevant data elements necessary for subsequent visualizations.
Project Point 2: In this stage, I conduct an in-depth analysis of the data to discern individual sales figures for each product line. Subsequently, I arrange the sales figures in descending order, which will prove instrumental in generating visualizations. Furthermore, I sort the sales data by year and deal size, facilitating a comprehensive understanding of the sales landscape.
Project Point 3: The subsequent objective entails identifying the most lucrative month for sales within a specified year and category. Thus far, the analysis has pinpointed November as the peak month for sales. Additionally, the classic product line emerges as the highest-grossing category.
Project Point 4: Leveraging the Recency-Frequency-Monetary (RFM) analysis technique, I appropriately segment customers based on three key metrics: recency of their last purchase, frequency of purchases, and the monetary value of transactions. During this analysis, I specifically focus on identifying lost customers and high-spending individuals who have shown a decline in recent purchases, as well as customers who exhibit consistent buying behavior at lower price points.
Project Point 5: An integral aspect of the project involves determining which two products are frequently sold together, enabling cross-selling opportunities and a deeper understanding of customer preferences.
Project Point 6: Finally, I generate compelling visualizations using Tableau, which effectively communicate the insights derived from the data analysis, offering a visually intuitive representation of the key findings. https://public.tableau.com/views/SQLSalesDataProject/SalesDash2?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link